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This Bayesian inference tool\ncalculates the evidence, with an associated error estimate, and produces\nposterior samples from distributions that may contain multiple modes and\npronounced (curving) degeneracies in high dimensions. The developments\npresented here lead to further substantial improvements in sampling efficiency\nand robustness, as compared to the original algorithm presented in Feroz &\nHobson (2008), which itself significantly outperformed existing MCMC techniques\nin a wide range of astrophysical inference problems. The accuracy and economy\nof the MultiNest algorithm is demonstrated by application to two toy problems\nand to a cosmological inference problem focussing on the extension of the\nvanilla $\\Lambda$CDM model to include spatial curvature and a varying equation\nof state for dark energy. The MultiNest software, which is fully parallelized\nusing MPI and includes an interface to CosmoMC, is available at\nhttp://www.mrao.cam.ac.uk/software/multinest/. It will also be released as part\nof the SuperBayeS package, for the analysis of supersymmetric theories of\nparticle physics, at http://www.superbayes.org","url_abs":"http://arxiv.org/abs/0809.3437v1","url_pdf":"http://arxiv.org/pdf/0809.3437v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multinest-an-efficient-and-robust-bayesian","repo_url":"https://github.com/AstroGidi/PyDynamicaLC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"multinest-an-efficient-and-robust-bayesian","repo_url":"https://github.com/JohannesBuchner/PyMultiNest","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"multinest-an-efficient-and-robust-bayesian","repo_url":"https://github.com/Joshuaalbert/jaxns","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"multinest-an-efficient-and-robust-bayesian","repo_url":"https://github.com/NicholasFarrow/GalacticDNSMass","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"multinest-an-efficient-and-robust-bayesian","repo_url":"https://github.com/SuperKam91/gns","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"multinest-an-efficient-and-robust-bayesian","repo_url":"https://github.com/mjt16/msci-multinest","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/0809.3437","atlas_url":"https://app.syntology.ai/?focus=0809.3437","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"0809.3437"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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